The present invention relates generally to gesture recognition, and more particularly to methods and apparatus for gesture recognition to be used in electronic games and other human-machine interface applications.
In many action-oriented electronic games, a player guides an animated character or a vehicle with an input device, such as a low-key-count keyboard, a joystick or an electronic pointing device, like a mouse. During the process, the character might have to avoid traps, to grab an object, or to fight an opponent. Typically, the player uses a joystick to control the direction of movement of the character, and buttons to activate preset actions, such as jumping and hitting. Though one can learn to manipulate such input devices, it is not natural to act through a joystick or a keyboard.
An alternative method to control animated characters in electronic games is through gesture recognition. In such a method, the player's gesture controls the character's motion. Typically, the player's gesture is captured by an optical or an infrared detector array, such as a video camera. The processor then analyzes the data from the array to identify the gesture, which is then used to control the character's motion. For example, in an on-going game, when the player kicks, a detector array captures the image of the kick and relates it to a processor, which analyzes the image and makes the character kick. This is a more intuitive way to play electronic games. It is much more direct and natural--when the player jumps, the character jumps accordingly.
However, prior art electronic game system implementing such image recognition techniques is relatively slow. One such system uses "dynamic" motion detection to recognize the player's images. In that method, a detector captures consecutive images, and a processor analyzes their differences. For example, as the player punches, the processor constantly compares consecutive images to find the path direction of the punch. The processor typically calculates the derivatives of the values of the pixels in the images. Then the processor extrapolates the direction of motion to identify future positions. Since the processor usually analyzes the images in greyscale, the "dynamic" motion detection technique requires significant amount of floating point multiplication. With significant number of images captured and intensive computation, such methods can accurately recognize gestures. However, this type of system can be slow.
There is still a need for an apparatus and a method that can quickly and accurately recognize a player's gestures.